Compare commits
4 Commits
| Author | SHA1 | Date |
|---|---|---|
 Alejandro Moreo Fernandez
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a387ede701 | |
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Alejandro Moreo Fernandez
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e6323bcd0b | |
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Alejandro Moreo Fernandez
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b3d5be0ac2 | |
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Alejandro Moreo Fernandez
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e0269ac625 |
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@ -17,7 +17,6 @@ for facilitating the analysis and interpretation of the experimental results.
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* Version 0.2.1 is released! major changes can be consulted [here](CHANGE_LOG.txt).
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* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/index.html)
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* Manuals are available [here](https://hlt-isti.github.io/QuaPy/manuals.html)
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### Installation
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1
TODO.txt
1
TODO.txt
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@ -15,7 +15,6 @@ scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
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- This functionality should be accessible via sampling protocols and evaluation functions
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- [TODO] document confidence in manuals
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- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
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- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
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- [TODO] add HistNetQ
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- [TODO] add CDE-iteration and Bayes-CDE methods
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@ -1,38 +0,0 @@
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"""
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Imagine we want to generate many samples out of a collection, that we want to distribute for others to run their
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own experiments in the very same test samples. One naive solution would come down to applying a given protocol to
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our collection (say the artificial prevalence protocol on the 'academic-success' UCI dataset), store all those samples
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on disk and make them available online. Distributing many such samples is undesirable.
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In this example, we generate the indexes that allow anyone to regenerate the samples out of the original collection.
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"""
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import quapy as qp
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from quapy.method.aggregative import PACC
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from quapy.protocol import UPP
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data = qp.datasets.fetch_UCIMulticlassDataset('academic-success')
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train, test = data.train_test
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# let us train a quantifier to check whether we can actually replicate the results
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quantifier = PACC()
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quantifier.fit(train)
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# let us simulate our experimental results
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protocol = UPP(test, sample_size=100, repeats=100, random_state=0)
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our_mae = qp.evaluation.evaluate(quantifier, protocol=protocol, error_metric='mae')
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print(f'We have obtained a MAE={our_mae:.3f}')
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# let us distribute the indexes; we specify that we want the indexes, not the samples
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protocol = UPP(test, sample_size=100, repeats=100, random_state=0, return_type='index')
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indexes = protocol.samples_parameters()
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# Imagine we distribute the indexes; now we show how to replicate our experiments.
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from quapy.protocol import ProtocolFromIndex
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data = qp.datasets.fetch_UCIMulticlassDataset('academic-success')
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train, test = data.train_test
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protocol = ProtocolFromIndex(data=test, indexes=indexes)
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their_mae = qp.evaluation.evaluate(quantifier, protocol=protocol, error_metric='mae')
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print(f'Another lab obtains a MAE={our_mae:.3f}')
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@ -1,56 +0,0 @@
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.neighbors import KNeighborsClassifier
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from statsmodels.sandbox.distributions.genpareto import quant
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import quapy as qp
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from quapy.protocol import UPP
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from quapy.method.aggregative import PACC, DMy, EMQ, KDEyML
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from quapy.method.meta import SCMQ, MCMQ, MCSQ
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import warnings
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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warnings.filterwarnings("ignore", category=ConvergenceWarning)
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qp.environ["SAMPLE_SIZE"]=100
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def train_and_test_model(quantifier, train, test):
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quantifier.fit(train)
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report = qp.evaluation.evaluation_report(quantifier, UPP(test), error_metrics=['mae', 'mrae'])
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print(quantifier.__class__.__name__)
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print(report.mean(numeric_only=True))
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quantifiers = [
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PACC(),
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DMy(),
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EMQ(),
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KDEyML()
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]
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classifier = LogisticRegression()
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dataset_name = qp.datasets.UCI_MULTICLASS_DATASETS[0]
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
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train, test = data.train_test
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scmq = SCMQ(classifier, quantifiers)
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train_and_test_model(scmq, train, test)
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# for quantifier in quantifiers:
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# train_and_test_model(quantifier, train, test)
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classifiers = [
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LogisticRegression(),
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KNeighborsClassifier(),
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# MultinomialNB()
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]
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mcmq = MCMQ(classifiers, quantifiers)
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train_and_test_model(mcmq, train, test)
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mcsq = MCSQ(classifiers, PACC())
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train_and_test_model(mcsq, train, test)
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@ -1,254 +0,0 @@
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from scipy.sparse import csc_matrix, csr_matrix
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from sklearn.base import BaseEstimator, TransformerMixin
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from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer, CountVectorizer
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import numpy as np
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from joblib import Parallel, delayed
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import sklearn
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import math
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from scipy.stats import t
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class ContTable:
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def __init__(self, tp=0, tn=0, fp=0, fn=0):
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self.tp=tp
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self.tn=tn
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self.fp=fp
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self.fn=fn
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def get_d(self): return self.tp + self.tn + self.fp + self.fn
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def get_c(self): return self.tp + self.fn
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def get_not_c(self): return self.tn + self.fp
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def get_f(self): return self.tp + self.fp
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def get_not_f(self): return self.tn + self.fn
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def p_c(self): return (1.0*self.get_c())/self.get_d()
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def p_not_c(self): return 1.0-self.p_c()
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def p_f(self): return (1.0*self.get_f())/self.get_d()
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def p_not_f(self): return 1.0-self.p_f()
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def p_tp(self): return (1.0*self.tp) / self.get_d()
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def p_tn(self): return (1.0*self.tn) / self.get_d()
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def p_fp(self): return (1.0*self.fp) / self.get_d()
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def p_fn(self): return (1.0*self.fn) / self.get_d()
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def tpr(self):
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c = 1.0*self.get_c()
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return self.tp / c if c > 0.0 else 0.0
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def fpr(self):
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_c = 1.0*self.get_not_c()
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return self.fp / _c if _c > 0.0 else 0.0
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def __ig_factor(p_tc, p_t, p_c):
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den = p_t * p_c
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if den != 0.0 and p_tc != 0:
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return p_tc * math.log(p_tc / den, 2)
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else:
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return 0.0
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def information_gain(cell):
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return __ig_factor(cell.p_tp(), cell.p_f(), cell.p_c()) + \
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__ig_factor(cell.p_fp(), cell.p_f(), cell.p_not_c()) +\
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__ig_factor(cell.p_fn(), cell.p_not_f(), cell.p_c()) + \
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__ig_factor(cell.p_tn(), cell.p_not_f(), cell.p_not_c())
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def squared_information_gain(cell):
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return information_gain(cell)**2
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def posneg_information_gain(cell):
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ig = information_gain(cell)
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if cell.tpr() < cell.fpr():
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return -ig
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else:
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return ig
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def pos_information_gain(cell):
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if cell.tpr() < cell.fpr():
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return 0
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else:
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return information_gain(cell)
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def pointwise_mutual_information(cell):
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return __ig_factor(cell.p_tp(), cell.p_f(), cell.p_c())
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def gss(cell):
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return cell.p_tp()*cell.p_tn() - cell.p_fp()*cell.p_fn()
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def chi_square(cell):
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den = cell.p_f() * cell.p_not_f() * cell.p_c() * cell.p_not_c()
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if den==0.0: return 0.0
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num = gss(cell)**2
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return num / den
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def conf_interval(xt, n):
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if n>30:
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z2 = 3.84145882069 # norm.ppf(0.5+0.95/2.0)**2
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else:
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z2 = t.ppf(0.5 + 0.95 / 2.0, df=max(n-1,1)) ** 2
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p = (xt + 0.5 * z2) / (n + z2)
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amplitude = 0.5 * z2 * math.sqrt((p * (1.0 - p)) / (n + z2))
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return p, amplitude
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def strength(minPosRelFreq, minPos, maxNeg):
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if minPos > maxNeg:
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return math.log(2.0 * minPosRelFreq, 2.0)
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else:
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return 0.0
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#set cancel_features=True to allow some features to be weighted as 0 (as in the original article)
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#however, for some extremely imbalanced dataset caused all documents to be 0
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def conf_weight(cell, cancel_features=False):
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c = cell.get_c()
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not_c = cell.get_not_c()
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tp = cell.tp
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fp = cell.fp
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pos_p, pos_amp = conf_interval(tp, c)
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neg_p, neg_amp = conf_interval(fp, not_c)
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min_pos = pos_p-pos_amp
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max_neg = neg_p+neg_amp
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den = (min_pos + max_neg)
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minpos_relfreq = min_pos / (den if den != 0 else 1)
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str_tplus = strength(minpos_relfreq, min_pos, max_neg);
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if str_tplus == 0 and not cancel_features:
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return 1e-20
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return str_tplus
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def get_tsr_matrix(cell_matrix, tsr_score_funtion):
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nC = len(cell_matrix)
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nF = len(cell_matrix[0])
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tsr_matrix = [[tsr_score_funtion(cell_matrix[c,f]) for f in range(nF)] for c in range(nC)]
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return np.array(tsr_matrix)
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def feature_label_contingency_table(positive_document_indexes, feature_document_indexes, nD):
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tp_ = len(positive_document_indexes & feature_document_indexes)
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fp_ = len(feature_document_indexes - positive_document_indexes)
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fn_ = len(positive_document_indexes - feature_document_indexes)
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tn_ = nD - (tp_ + fp_ + fn_)
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return ContTable(tp=tp_, tn=tn_, fp=fp_, fn=fn_)
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def category_tables(feature_sets, category_sets, c, nD, nF):
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return [feature_label_contingency_table(category_sets[c], feature_sets[f], nD) for f in range(nF)]
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def get_supervised_matrix(coocurrence_matrix, label_matrix, n_jobs=-1):
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"""
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Computes the nC x nF supervised matrix M where Mcf is the 4-cell contingency table for feature f and class c.
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Efficiency O(nF x nC x log(S)) where S is the sparse factor
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"""
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nD, nF = coocurrence_matrix.shape
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nD2, nC = label_matrix.shape
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if nD != nD2:
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raise ValueError('Number of rows in coocurrence matrix shape %s and label matrix shape %s is not consistent' %
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(coocurrence_matrix.shape,label_matrix.shape))
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def nonzero_set(matrix, col):
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return set(matrix[:, col].nonzero()[0])
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if isinstance(coocurrence_matrix, csr_matrix):
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coocurrence_matrix = csc_matrix(coocurrence_matrix)
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feature_sets = [nonzero_set(coocurrence_matrix, f) for f in range(nF)]
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category_sets = [nonzero_set(label_matrix, c) for c in range(nC)]
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cell_matrix = Parallel(n_jobs=n_jobs, backend="threading")(
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delayed(category_tables)(feature_sets, category_sets, c, nD, nF) for c in range(nC)
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)
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return np.array(cell_matrix)
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class TSRweighting(BaseEstimator,TransformerMixin):
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"""
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Supervised Term Weighting function based on any Term Selection Reduction (TSR) function (e.g., information gain,
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chi-square, etc.) or, more generally, on any function that could be computed on the 4-cell contingency table for
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each category-feature pair.
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The supervised_4cell_matrix is a `(n_classes, n_words)` matrix containing the 4-cell contingency tables
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for each class-word pair, and can be pre-computed (e.g., during the feature selection phase) and passed as an
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argument.
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When `n_classes>1`, i.e., in multiclass scenarios, a global_policy is used in order to determine a
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single feature-score which informs about its relevance. Accepted policies include "max" (takes the max score
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across categories), "ave" and "wave" (take the average, or weighted average, across all categories -- weights
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correspond to the class prevalence), and "sum" (which sums all category scores).
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"""
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def __init__(self, tsr_function, global_policy='max', supervised_4cell_matrix=None, sublinear_tf=True, norm='l2', min_df=3, n_jobs=-1):
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if global_policy not in ['max', 'ave', 'wave', 'sum']: raise ValueError('Global policy should be in {"max", "ave", "wave", "sum"}')
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self.tsr_function = tsr_function
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self.global_policy = global_policy
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self.supervised_4cell_matrix = supervised_4cell_matrix
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self.sublinear_tf = sublinear_tf
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self.norm = norm
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self.min_df = min_df
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self.n_jobs = n_jobs
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def fit(self, X, y):
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self.count_vectorizer = CountVectorizer(min_df=self.min_df)
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X = self.count_vectorizer.fit_transform(X)
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self.tf_vectorizer = TfidfTransformer(
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norm=None, use_idf=False, smooth_idf=False, sublinear_tf=self.sublinear_tf
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).fit(X)
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if len(y.shape) == 1:
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y = np.expand_dims(y, axis=1)
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nD, nC = y.shape
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nF = len(self.tf_vectorizer.get_feature_names_out())
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if self.supervised_4cell_matrix is None:
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self.supervised_4cell_matrix = get_supervised_matrix(X, y, n_jobs=self.n_jobs)
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else:
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if self.supervised_4cell_matrix.shape != (nC, nF):
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raise ValueError("Shape of supervised information matrix is inconsistent with X and y")
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tsr_matrix = get_tsr_matrix(self.supervised_4cell_matrix, self.tsr_function)
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if self.global_policy == 'ave':
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self.global_tsr_vector = np.average(tsr_matrix, axis=0)
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elif self.global_policy == 'wave':
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category_prevalences = [sum(y[:,c])*1.0/nD for c in range(nC)]
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self.global_tsr_vector = np.average(tsr_matrix, axis=0, weights=category_prevalences)
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elif self.global_policy == 'sum':
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self.global_tsr_vector = np.sum(tsr_matrix, axis=0)
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elif self.global_policy == 'max':
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self.global_tsr_vector = np.amax(tsr_matrix, axis=0)
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return self
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def fit_transform(self, X, y):
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return self.fit(X,y).transform(X)
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def transform(self, X):
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if not hasattr(self, 'global_tsr_vector'): raise NameError('TSRweighting: transform method called before fit.')
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X = self.count_vectorizer.transform(X)
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tf_X = self.tf_vectorizer.transform(X).toarray()
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weighted_X = np.multiply(tf_X, self.global_tsr_vector)
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if self.norm is not None and self.norm!='none':
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weighted_X = sklearn.preprocessing.normalize(weighted_X, norm=self.norm, axis=1, copy=False)
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return csr_matrix(weighted_X)
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@ -1,208 +0,0 @@
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from scipy.sparse import issparse
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from sklearn.decomposition import TruncatedSVD
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from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler
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import quapy as qp
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from data import LabelledCollection
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import numpy as np
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from experimental_non_aggregative.custom_vectorizers import *
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from method._kdey import KDEBase
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from protocol import APP
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from quapy.method.aggregative import HDy, DistributionMatchingY
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from quapy.method.base import BaseQuantifier
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from scipy import optimize
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import pandas as pd
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import quapy.functional as F
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# TODO: explore the bernoulli (term presence/absence) variant
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# TODO: explore the multinomial (term frequency) variant
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# TODO: explore the multinomial + length normalization variant
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# TODO: consolidate the TSR-variant (e.g., using information gain) variant;
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# - works better with the idf?
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# - works better with length normalization?
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# - etc
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class DxS(BaseQuantifier):
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def __init__(self, vectorizer=None, divergence='topsoe'):
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self.vectorizer = vectorizer
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self.divergence = divergence
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# def __as_distribution(self, instances):
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# return np.asarray(instances.sum(axis=0) / instances.sum()).flatten()
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def __as_distribution(self, instances):
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dist = instances.mean(axis=0)
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return np.asarray(dist).flatten()
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def fit(self, text_instances, labels):
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classes = np.unique(labels)
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if self.vectorizer is not None:
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text_instances = self.vectorizer.fit_transform(text_instances, y=labels)
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distributions = []
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for class_i in classes:
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distributions.append(self.__as_distribution(text_instances[labels == class_i]))
|
||||
|
||||
self.validation_distribution = np.asarray(distributions)
|
||||
|
||||
return self
|
||||
|
||||
def predict(self, text_instances):
|
||||
if self.vectorizer is not None:
|
||||
text_instances = self.vectorizer.transform(text_instances)
|
||||
|
||||
test_distribution = self.__as_distribution(text_instances)
|
||||
divergence = qp.functional.get_divergence(self.divergence)
|
||||
n_classes, n_feats = self.validation_distribution.shape
|
||||
|
||||
def match(prev):
|
||||
prev = np.expand_dims(prev, axis=0)
|
||||
mixture_distribution = (prev @ self.validation_distribution).flatten()
|
||||
return divergence(test_distribution, mixture_distribution)
|
||||
|
||||
# the initial point is set as the uniform distribution
|
||||
uniform_distribution = np.full(fill_value=1 / n_classes, shape=(n_classes,))
|
||||
|
||||
# solutions are bounded to those contained in the unit-simplex
|
||||
bounds = tuple((0, 1) for x in range(n_classes)) # values in [0,1]
|
||||
constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)}) # values summing up to 1
|
||||
r = optimize.minimize(match, x0=uniform_distribution, method='SLSQP', bounds=bounds, constraints=constraints)
|
||||
return r.x
|
||||
|
||||
|
||||
|
||||
class KDExML(BaseQuantifier, KDEBase):
|
||||
|
||||
def __init__(self, bandwidth=0.1, standardize=False):
|
||||
self._check_bandwidth(bandwidth)
|
||||
self.bandwidth = bandwidth
|
||||
self.standardize = standardize
|
||||
|
||||
def fit(self, X, y):
|
||||
classes = sorted(np.unique(y))
|
||||
|
||||
if self.standardize:
|
||||
self.scaler = StandardScaler()
|
||||
X = self.scaler.fit_transform(X)
|
||||
|
||||
if issparse(X):
|
||||
X = X.toarray()
|
||||
|
||||
self.mix_densities = self.get_mixture_components(X, y, classes, self.bandwidth)
|
||||
return self
|
||||
|
||||
def predict(self, X):
|
||||
"""
|
||||
Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood
|
||||
of the data (i.e., that minimizes the negative log-likelihood)
|
||||
|
||||
:param X: instances in the sample
|
||||
:return: a vector of class prevalence estimates
|
||||
"""
|
||||
epsilon = 1e-10
|
||||
if issparse(X):
|
||||
X = X.toarray()
|
||||
n_classes = len(self.mix_densities)
|
||||
if self.standardize:
|
||||
X = self.scaler.transform(X)
|
||||
test_densities = [self.pdf(kde_i, X) for kde_i in self.mix_densities]
|
||||
|
||||
def neg_loglikelihood(prev):
|
||||
test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip (prev, test_densities))
|
||||
test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
|
||||
return -np.sum(test_loglikelihood)
|
||||
|
||||
return F.optim_minimize(neg_loglikelihood, n_classes)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = 250
|
||||
qp.environ['N_JOBS'] = -1
|
||||
min_df = 10
|
||||
# dataset = 'imdb'
|
||||
repeats = 10
|
||||
error = 'mae'
|
||||
|
||||
div = 'topsoe'
|
||||
|
||||
# generates tuples (dataset, method, method_name)
|
||||
# (the dataset is needed for methods that process the dataset differently)
|
||||
def gen_methods():
|
||||
|
||||
for dataset in qp.datasets.REVIEWS_SENTIMENT_DATASETS:
|
||||
|
||||
data = qp.datasets.fetch_reviews(dataset, tfidf=False)
|
||||
|
||||
# bernoulli_vectorizer = CountVectorizer(min_df=min_df, binary=True)
|
||||
# dxs = DxS(divergence=div, vectorizer=bernoulli_vectorizer)
|
||||
# yield data, dxs, 'DxS-Bernoulli'
|
||||
#
|
||||
# multinomial_vectorizer = CountVectorizer(min_df=min_df, binary=False)
|
||||
# dxs = DxS(divergence=div, vectorizer=multinomial_vectorizer)
|
||||
# yield data, dxs, 'DxS-multinomial'
|
||||
#
|
||||
# tf_vectorizer = TfidfVectorizer(sublinear_tf=False, use_idf=False, min_df=min_df, norm=None)
|
||||
# dxs = DxS(divergence=div, vectorizer=tf_vectorizer)
|
||||
# yield data, dxs, 'DxS-TF'
|
||||
#
|
||||
# logtf_vectorizer = TfidfVectorizer(sublinear_tf=True, use_idf=False, min_df=min_df, norm=None)
|
||||
# dxs = DxS(divergence=div, vectorizer=logtf_vectorizer)
|
||||
# yield data, dxs, 'DxS-logTF'
|
||||
#
|
||||
# tfidf_vectorizer = TfidfVectorizer(use_idf=True, min_df=min_df, norm=None)
|
||||
# dxs = DxS(divergence=div, vectorizer=tfidf_vectorizer)
|
||||
# yield data, dxs, 'DxS-TFIDF'
|
||||
#
|
||||
# tfidf_vectorizer = TfidfVectorizer(use_idf=True, min_df=min_df, norm='l2')
|
||||
# dxs = DxS(divergence=div, vectorizer=tfidf_vectorizer)
|
||||
# yield data, dxs, 'DxS-TFIDF-l2'
|
||||
|
||||
tsr_vectorizer = TSRweighting(tsr_function=information_gain, min_df=min_df, norm='l2')
|
||||
dxs = DxS(divergence=div, vectorizer=tsr_vectorizer)
|
||||
yield data, dxs, 'DxS-TFTSR-l2'
|
||||
|
||||
data = qp.datasets.fetch_reviews(dataset, tfidf=True, min_df=min_df)
|
||||
|
||||
kdex = KDExML()
|
||||
reduction = TruncatedSVD(n_components=100, random_state=0)
|
||||
red_data = qp.data.preprocessing.instance_transformation(data, transformer=reduction, inplace=False)
|
||||
yield red_data, kdex, 'KDEx'
|
||||
|
||||
hdy = HDy(LogisticRegression())
|
||||
yield data, hdy, 'HDy'
|
||||
|
||||
# dm = DistributionMatchingY(LogisticRegression(), divergence=div, nbins=5)
|
||||
# yield data, dm, 'DM-5b'
|
||||
#
|
||||
# dm = DistributionMatchingY(LogisticRegression(), divergence=div, nbins=10)
|
||||
# yield data, dm, 'DM-10b'
|
||||
|
||||
|
||||
|
||||
|
||||
result_path = 'results.csv'
|
||||
with open(result_path, 'wt') as csv:
|
||||
csv.write(f'Method\tDataset\tMAE\tMRAE\n')
|
||||
for data, quantifier, quant_name in gen_methods():
|
||||
quantifier.fit(*data.training.Xy)
|
||||
report = qp.evaluation.evaluation_report(quantifier, APP(data.test, repeats=repeats), error_metrics=['mae','mrae'], verbose=True)
|
||||
means = report.mean(numeric_only=True)
|
||||
csv.write(f'{quant_name}\t{data.name}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\n')
|
||||
|
||||
df = pd.read_csv(result_path, sep='\t')
|
||||
# print(df)
|
||||
|
||||
pv = df.pivot_table(index='Method', columns="Dataset", values=["MAE", "MRAE"])
|
||||
print(pv)
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue